French Validation of the Brief Negative Symptom Scale (BNSS)
Bibliographic record
Abstract
Objectives This study aims to validate the French version of the Brief Negative Symptom Scale (BNSS) by assessing its psychometric properties in a population of patients with schizophrenia or schizoaffective disorder. Methods 73 patients with schizophrenia or schizoaffective disorder were included. Participants were evaluated using the BNSS, the Positive and Negative Syndrome Scale (PANSS), and the Self-Evaluation of Negative Symptoms (SNS). The internal consistency of the BNSS was measured using Cronbach's alpha, structural validity was assessed through exploratory factor analysis, and construct validity was evaluated with Spearman correlations between BNSS scores, the negative subscale of the PANSS, the total SNS score, the positive subscale of the PANSS, and PANSS items evaluating insight and depressive mood. Results The internal consistency of the BNSS was excellent (Cronbach's alpha = 0.93). Exploratory factor analysis revealed two factors corresponding to the motivational and expressive dimensions of negative symptoms. Significant positive correlations were found between total BNSS scores and the negative subscale of the PANSS (Rho = 0.77; p < 0.001), as well as with SNS scores (Rho = 0.55; p < 0.001). No correlation was observed between total BNSS scores and the positive subscales of the PANSS (Rho = 0.09; p = 0.41). However, significant positive correlations were noted with the PANSS item assessing depression (Rho = 0.28; p = 0.015) and insight (Rho = 0.43; p < 0.001). Conclusion The French version of the BNSS has demonstrated strong psychometric properties and is suitable for clinical and research use. Plain Language Summary Title Validation d’une échelle d’évaluation des symptômes négatifs, la « Brief Negative Symptom Scale » (BNSS)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".